CN112348091A - Dual-clustering blackwork site identification algorithm based on GPS (global positioning system) of muck truck - Google Patents
Dual-clustering blackwork site identification algorithm based on GPS (global positioning system) of muck truck Download PDFInfo
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Abstract
The invention discloses a blackjack site recognition algorithm based on double clustering of a muck truck GPS, which comprises the steps of firstly obtaining GPS position data and license plate information of a plurality of muck trucks in an area, obtaining known GPS position data of a construction site, GPS position data of a parking lot, GPS position data of a storage area and GPS position data of a gas station in the area, and numbering all the data according to categories; then obtaining each clustering center based on an algorithm of DBScan and an algorithm of k _ means, and comparing a preset threshold value to obtain a suspected black site location; the method comprises the steps of utilizing GPS data of a plurality of muck vehicles and GPS data of known construction sites, parking lots, absorption sites and gas stations, combining a DBSCAn algorithm and a k _ means algorithm to perform clustering, comparing threshold values, and identifying GPS gathering places and unknown black site points of the plurality of muck vehicles so as to detect and identify black sites in time for management and control.
Description
Technical Field
The invention belongs to the technical field of urban environment management, and mainly relates to a double-clustering blackjack site identification algorithm based on a GPS (global positioning system) of a muck truck.
Background
At present of the rapid development of cities, every city is newly built every day, the waste is abandoned, the soil is exploited, the construction site appears like bamboo shoots after raining, but some developers have the situation of concealing the construction site, so that the barriers to the management and control of the construction site are caused, if only the manual work is used for checking one by one according to GPS point positions, or the workers go to the city to check whether each construction site is legal, not only the manual work and material resources are increased, but also much time is wasted, therefore, by using the GPS coordinate point position information of the residue soil truck reasonably, the unknown gathering points of the black site and other vehicles are screened out, and the problem of timely detecting and identifying the concealed construction site (hereinafter referred to as the black site) to manage and control the force is solved.
Disclosure of Invention
The invention aims to provide a black work site identification algorithm based on double clustering of a GPS of a slag car, aiming at the defects and shortcomings of the prior art, the GPS data of the slag car and the GPS data of a known site are utilized for gathering, and black work site sites are identified according to a network map so as to be processed in time.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a double-clustering blackjack site recognition algorithm based on a GPS of a muck truck comprises the following steps:
s1, acquiring GPS position data and license plate information of a plurality of muck vehicles in the area;
s2, acquiring known construction site GPS position data, parking lot GPS position data, storage area GPS position data and gas station GPS position data in the area, and numbering all the data according to categories to form a table;
s3, classifying data by using the dregs car license plate, carrying out cubic clustering according to the GPS position data of the dregs car, removing isolated points after each clustering, and combining all data into a table;
s4 clustering the data in S3 again based on DBScan algorithm, and adding the clustering center point of each class of DBScan;
s5 clustering and dividing various types of numbered building type data in S2 based on a k _ means algorithm to obtain a k _ means model and a clustering center point generated by k _ means, wherein the cluster number is the total number of all building types;
s6, predicting the building type to which the data in S4 belongs according to the k _ means model obtained in S5, and adding the k _ means cluster center point to the data;
s7, calculating the distance between the DBSCAn cluster center point obtained in S4 and the cluster center point obtained in S6 through a k _ means algorithm, and adding the DBSCAn cluster center point to the table and marking the DBSCAn cluster center point as an unknown point when the distance is larger than a preset value; adding the known building number to the table when the distance is smaller than the preset value;
s8, acquiring coordinate points of all roads in the area through the network map, calculating the distance between the unknown point and the coordinate point of the road in S7, and when the distance is smaller than a preset threshold value, regarding the unknown point as being on the road, and adding a mark of 'in the road' in the table; when the distance is larger than a preset threshold value, the unknown point is regarded as not on the road;
s9 deleting the unknown point marked as 'in road' in the table and keeping the unknown point not on the road;
s10 identifies an unknown point not on the road as a suspected black site.
Further, the muck vehicle with the same license plate number is calculated in the same building code according to the data obtained in the S7
Entry time under number, exit time and dwell time.
Further, when two or more unknown point locations are obtained according to S7, the distances between two adjacent unknown point locations are calculated, and when the distance is smaller than a predetermined threshold, the two adjacent unknown point locations are merged into one point, and when the distance is larger than the predetermined threshold, the two adjacent unknown point locations are not merged; and comparing a plurality of continuous unknown points, and when a certain point repeatedly appears, namely the vehicle repeatedly moves back and forth at the point, the unknown point is a suspected black site.
The invention has the beneficial effects that: the method comprises the steps of utilizing GPS data of a plurality of muck vehicles and GPS data of known construction sites, parking lots, absorption sites and gas stations, combining a DBSCAn algorithm and a k _ means algorithm to perform clustering, comparing threshold values, and identifying GPS gathering places and unknown black site points of the plurality of muck vehicles so as to detect and identify black sites in time for management and control.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments, it should be understood that the embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.
A double-clustering blackjack site recognition algorithm based on a GPS of a muck truck comprises the following steps:
s1, acquiring GPS position data and license plate information of a plurality of muck vehicles in the area;
s2, acquiring known construction site GPS position data, parking lot GPS position data, storage area GPS position data and gas station GPS position data in the area, and numbering all the data according to categories to form a table;
s3, classifying data by using the dregs car license plate, carrying out cubic clustering according to the GPS position data of the dregs car, removing isolated points after each clustering, and combining all data into a table;
s4 clustering the data in S3 again based on DBScan algorithm, and adding the clustering center point of each class of DBScan;
s5 clustering and dividing various types of numbered building type data in S2 based on a k _ means algorithm to obtain a k _ means model and a clustering center point generated by k _ means, wherein the cluster number is the total number of all building types;
s6, predicting the building type to which the data in S4 belongs according to the k _ means model obtained in S5, and adding the k _ means cluster center point to the data;
s7, calculating the distance between the DBSCAn cluster center point obtained in S4 and the cluster center point obtained in S6 through a k _ means algorithm, and adding the DBSCAn cluster center point to the table and marking the DBSCAn cluster center point as an unknown point when the distance is larger than a preset value; adding the known building number to the table when the distance is smaller than the preset value;
s8, acquiring coordinate points of all roads in the area through the network map, calculating the distance between the unknown point and the coordinate point of the road in S7, and when the distance is smaller than a preset threshold value, regarding the unknown point as being on the road, and adding a mark of 'in the road' in the table; when the distance is larger than a preset threshold value, the unknown point is regarded as not on the road;
s9 deleting the unknown point marked as 'in road' in the table and keeping the unknown point not on the road;
s10 identifies an unknown point not on the road as a suspected black site.
Further, the muck vehicle with the same license plate number is calculated in the same building code according to the data obtained in the S7
Entry time under number, exit time and dwell time.
Further, when two or more unknown point locations are obtained according to S7, the distances between two adjacent unknown point locations are calculated, and when the distance is smaller than a predetermined threshold, the two adjacent unknown point locations are merged into one point, and when the distance is larger than the predetermined threshold, the two adjacent unknown point locations are not merged; and comparing a plurality of continuous unknown points, and when a certain point repeatedly appears, namely the vehicle repeatedly moves back and forth at the point, the unknown point is a suspected black site.
The description and application of the present invention are intended to be illustrative and exemplary only, and are not intended to limit the scope of the invention to the embodiments described above. Variations and modifications of the embodiments disclosed herein are fully possible, and alternative and equivalent various components of the embodiments are well known to those skilled in the art. It will also be apparent to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, and that other modifications and variations of the embodiments disclosed herein, without departing from the spirit or essential characteristics thereof.
Claims (3)
1. The double-clustering blackwork site identification algorithm based on the GPS of the muck truck is characterized in that: the method comprises the following steps:
s1, acquiring GPS position data and license plate information of a plurality of muck vehicles in the area;
s2, acquiring known construction site GPS position data, parking lot GPS position data, storage area GPS position data and gas station GPS position data in the area, and numbering all the data according to categories to form a table;
s3, classifying data by using the dregs car license plate, carrying out cubic clustering according to the GPS position data of the dregs car, removing isolated points after each clustering, and combining all data into a table;
s4 clustering the data in S3 again based on DBScan algorithm, and adding the clustering center point of each class of DBScan;
s5 clustering and dividing various types of numbered building type data in S2 based on a k _ means algorithm to obtain a k _ means model and a clustering center point generated by k _ means, wherein the cluster number is the total number of all building types;
s6, predicting the building type to which the data in S4 belongs according to the k _ means model obtained in S5, and adding the k _ means cluster center point to the data;
s7, calculating the distance between the DBSCAn cluster center point obtained in S4 and the cluster center point obtained in S6 through a k _ means algorithm, and adding the DBSCAn cluster center point to the table and marking the DBSCAn cluster center point as an unknown point when the distance is larger than a preset value; adding the known building number to the table when the distance is smaller than the preset value;
s8, acquiring coordinate points of all roads in the area through the network map, calculating the distance between the unknown point and the coordinate point of the road in S7, and when the distance is smaller than a preset threshold value, regarding the unknown point as being on the road, and adding a mark of 'in the road' in the table; when the distance is larger than a preset threshold value, the unknown point is regarded as not on the road;
s9 deleting the unknown point marked as 'in road' in the table and keeping the unknown point not on the road;
s10 identifies an unknown point not on the road as a suspected black site.
2. The dual-cluster blackjack site identification algorithm based on a GPS of a slag car according to claim 1, wherein: and calculating the entering time, the leaving time and the staying time of the muck vehicle with the same license plate number under the same building number according to the data obtained in the S7.
3. The dual-cluster blackjack site identification algorithm based on a GPS of a slag car according to claim 1, wherein: calculating the distance between two adjacent unknown point positions according to the two or more unknown point positions obtained in the step S7, merging the two adjacent unknown point positions into one point when the distance is smaller than a preset threshold value, and not merging the two points when the distance is larger than the preset threshold value; and comparing a plurality of continuous unknown points, and when a certain point repeatedly appears, namely the vehicle repeatedly moves back and forth at the point, the unknown point is a suspected black site.
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| CN113392903A (en) * | 2021-06-15 | 2021-09-14 | 上海华兴数字科技有限公司 | Method, system and device for identifying construction site area |
| CN113610008A (en) * | 2021-08-10 | 2021-11-05 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for acquiring state of slag car |
| CN113689102A (en) * | 2021-08-17 | 2021-11-23 | 武汉依迅北斗时空技术股份有限公司 | Construction site monitoring mode determining method and system |
| CN113821735A (en) * | 2021-08-18 | 2021-12-21 | 北京中交兴路信息科技有限公司 | Method, device, equipment and storage medium for identifying illegal refueling station |
| CN115457777A (en) * | 2022-09-06 | 2022-12-09 | 北京商海文天科技发展有限公司 | Specific vehicle traceability analysis method |
| CN116486639A (en) * | 2023-06-14 | 2023-07-25 | 眉山环天智慧科技有限公司 | Vehicle supervision method based on remote sensing and Beidou satellite data analysis |
| CN117131149A (en) * | 2023-10-26 | 2023-11-28 | 四川国蓝中天环境科技集团有限公司 | Earth and rock point location and transportation network identification method based on GPS track of slag transport vehicle |
| CN117494011A (en) * | 2023-12-29 | 2024-02-02 | 四川国蓝中天环境科技集团有限公司 | Dust raising point position type discriminating method based on earth and stone transport characteristics of slag transport vehicle |
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| CN117131149A (en) * | 2023-10-26 | 2023-11-28 | 四川国蓝中天环境科技集团有限公司 | Earth and rock point location and transportation network identification method based on GPS track of slag transport vehicle |
| CN117131149B (en) * | 2023-10-26 | 2024-01-23 | 四川国蓝中天环境科技集团有限公司 | Earth and rock point location and transportation network identification method based on GPS track of slag transport vehicle |
| CN117494011A (en) * | 2023-12-29 | 2024-02-02 | 四川国蓝中天环境科技集团有限公司 | Dust raising point position type discriminating method based on earth and stone transport characteristics of slag transport vehicle |
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